單變量LSTM預測模型（5）
前置教程：
更健壯的LSTM案例
基於Python時間序列LSTM預測系列教程（4）描述的案例，進行改進
Keras每次會默認隨機初始化LSTM
爲了評估模型的性能，可以多次試驗，求RMSE的均值
代碼解析
#重複實驗
repeats = 30
error_scores = list()
for r in range(repeats):
#fit 模型
lstm_model = fit_lstm(train_scaled, 1, 3000, 4)#訓練數據，batch_size，epoche次數, 神經元個數
#預測
train_reshaped = train_scaled[:,0].reshape(len(train_scaled), 1, 1)
lstm_model.predict(train_reshaped, batch_size=1)
#測試數據的前向驗證
predictions = list()
for i in range(len(test_scaled)):
#1步長預測
X, y = test_scaled[i, 0:-1], test_scaled[i, -1]
yhat = forcast_lstm(lstm_model, 1, X)
#逆縮放
yhat = invert_scale(scaler, X, yhat)
#逆差分
yhat = inverse_difference(raw_values, yhat, len(test_scaled)+1-i)
predictions.append(yhat)
expected = raw_values[len(train)+i+1]
print('Moth=%d, Predicted=%f, Expected=%f'%(i+1, yhat, expected))
#性能報告
rmse = sqrt(mean_squared_error(raw_values[-12:], predictions))
print('%d) Test RMSE:%.3f' %(r+1,rmse))
error_scores.append(rmse)
#統計信息
results = DataFrame()
results['rmse'] = error_scores
print(results.describe())
results.boxplot()
pyplot.show()
實驗結果
